Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems

In this paper, it proposes a multi-population interactive coevolutionary algorithm for the flexible job shop scheduling problems. In the proposed algorithm, both the ant colony optimization and genetic algorithm with different configurations were applied to evolve each population independently. By t...

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Bibliographic Details
Published in:Computational optimization and applications 2011, Vol.48 (1), p.139-155
Main Authors: Xing, Li-Ning, Chen, Ying-Wu, Yang, Ke-Wei
Format: Article
Language:English
Subjects:
Algorithms
Benchmarking
Colonies
Competition
Computation
Convex and Discrete Geometry
Employment
Genetic algorithms
Graduate students
Heuristic
Interactive
Job shop scheduling
Job shops
Management Science
Mathematics
Mathematics and Statistics
Operations Research
Operations Research/Decision Theory
Optimization
Population
Production scheduling
Scheduling
Statistics
Studies
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Summary:In this paper, it proposes a multi-population interactive coevolutionary algorithm for the flexible job shop scheduling problems. In the proposed algorithm, both the ant colony optimization and genetic algorithm with different configurations were applied to evolve each population independently. By the interaction, competition and sharing mechanism among populations, the computing resource is utilized more efficiently, and the quality of populations is improved effectively. The performance of our proposed approach was evaluated by a lot of benchmark instances taken from literature. The experimental results have shown that the proposed algorithm is a feasible and effective approach for the flexible job shop scheduling problem.
ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-009-9244-7